Forecasting agricultural markets remains a core challenge in business analytics, where nonlinear dynamics, structural breaks, and sparse data have historically limited the gains from increasingly complex econometric and machine learning models. As a result, a long-standing belief in the literature is that simple time-series methods often outperform more advanced alternatives. This paper provides the first systematic evidence that this belief no longer holds in the modern era of time-series foundation models (TSFMs). Using USDA ERS data from 1997-2025, we evaluate 17 forecasting approaches across four model classes, assessing monthly forecasting performance and benchmarking against Market Year Average (MYA) price predictions. This period spans multiple agricultural cycles, major policy changes, and major market disruptions, with substantial cross-commodity price volatility. Focusing on five state-of-the-art TSFMs, we show that zero-shot foundation models (with only historical prices and without any additional covariates) consistently outperform traditional time-series methods, machine learning models, and deep learning architectures trained from scratch. Among them, Time-MoE delivers the largest accuracy gains, improving forecasts by 45% (MAE) overall and by more than 50% for corn and soybeans relative to USDA benchmarks. These results point to a paradigm shift in agricultural forecasting: while earlier generations of advanced models struggled to surpass simple benchmarks, modern pre-trained foundation models achieve substantial and robust improvements, offering a scalable and powerful new framework for highstakes predictive analytics.

翻译：农业市场预测始终是商业分析领域的核心挑战，非线性动态、结构性突变和稀疏数据历来限制了日益复杂的计量经济学与机器学习模型的性能提升。因此，文献中长期存在一种共识：简单的时序方法往往优于更先进的替代方案。本文首次系统性地证明，在时序基础模型（TSFMs）时代，这一共识已不再成立。利用美国农业部经济研究局（USDA ERS）1997-2025年的数据，我们评估了四大模型类别中的17种预测方法，以月度预测性能为评估标准，并以市场年度平均（MYA）价格预测作为基准。这一时期涵盖了多个农业周期、重大政策调整和市场剧烈波动，呈现出显著的跨商品价格波动性。聚焦于五种最先进的时序基础模型，我们发现零样本基础模型（仅使用历史价格数据且不引入任何额外协变量）在预测性能上持续超越传统时序方法、机器学习模型以及从头训练的深度学习架构。其中，Time-MoE模型取得了最大的精度提升，其整体平均绝对误差（MAE）较美国农业部基准降低了45%，在玉米和大豆预测中更实现了超过50%的改进。这些结果表明农业预测领域正经历范式转变：尽管早期先进模型难以超越简单基准，但现代预训练基础模型实现了显著且稳健的性能提升，为高风险的预测分析提供了一个可扩展且强大的新框架。